------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\eel19\Dropbox\Classes\Paper Projects\Police Militarization and Shootings\Paper\PRQ Final Log.log
  log type:  text
 opened on:  23 May 2018, 15:46:20

. use "C:\Users\eel19\Dropbox\Classes\Paper Projects\Police Militarization and Shootings\Data\finalized\Re-do\final.dta"
>  

. zinb count lag_mil_div POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv county
> wide, inflate(lag_mil_div POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv cou
> ntywide) vce(cluster state_county_agency)

Fitting constant-only model:

Iteration 0:   log pseudolikelihood =   -3686.21  (not concave)
Iteration 1:   log pseudolikelihood = -3467.8757  
Iteration 2:   log pseudolikelihood = -3326.4853  
Iteration 3:   log pseudolikelihood = -3132.4514  
Iteration 4:   log pseudolikelihood = -2964.5729  
Iteration 5:   log pseudolikelihood = -2882.1387  
Iteration 6:   log pseudolikelihood = -2842.8547  
Iteration 7:   log pseudolikelihood = -2833.5342  
Iteration 8:   log pseudolikelihood = -2831.4175  
Iteration 9:   log pseudolikelihood = -2831.3724  
Iteration 10:  log pseudolikelihood = -2831.3723  

Fitting full model:

Iteration 0:   log pseudolikelihood = -2831.3723  
Iteration 1:   log pseudolikelihood = -2592.4235  
Iteration 2:   log pseudolikelihood = -2570.6256  
Iteration 3:   log pseudolikelihood =  -2569.611  
Iteration 4:   log pseudolikelihood = -2569.5508  
Iteration 5:   log pseudolikelihood = -2569.5505  
Iteration 6:   log pseudolikelihood = -2569.5505  

Zero-inflated negative binomial regression      Number of obs     =     11,848
                                                Nonzero obs       =        716
                                                Zero obs          =     11,132

Inflation model      = logit                    Wald chi2(8)      =     255.18
Log pseudolikelihood =  -2569.55                Prob > chi2       =     0.0000

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
count                 |
          lag_mil_div |   .0007958   .0004065     1.96   0.050    -8.43e-07    .0015925
           POP2012div |   .0050536   .0023819     2.12   0.034     .0003852    .0097221
      percent_poverty |    .004912   .0164402     0.30   0.765    -.0273102    .0371342
         percent_hisp |   .0111428   .0083088     1.34   0.180    -.0051423    .0274278
percent_black_nothisp |   .0035543   .0056345     0.63   0.528     -.007489    .0145976
           crime_rate |   .0193772   .0086433     2.24   0.025     .0024367    .0363178
      budgetpercapdiv |   .0003342   .0139644     0.02   0.981    -.0270356     .027704
           countywide |  -1.530097   .1873384    -8.17   0.000    -1.897274   -1.162921
                _cons |  -1.339846   .4805108    -2.79   0.005    -2.281629   -.3980619
----------------------+----------------------------------------------------------------
inflate               |
          lag_mil_div |  -.0013738   .0023847    -0.58   0.565    -.0060478    .0033001
           POP2012div |  -.2223874   .0628048    -3.54   0.000    -.3454825   -.0992923
      percent_poverty |  -.0052721   .0206263    -0.26   0.798    -.0456989    .0351548
         percent_hisp |   .0058628    .011373     0.52   0.606    -.0164279    .0281534
percent_black_nothisp |  -.0022605   .0074985    -0.30   0.763    -.0169574    .0124363
           crime_rate |   .0030244   .0144661     0.21   0.834    -.0253287    .0313775
      budgetpercapdiv |  -.0150141    .027259    -0.55   0.582    -.0684407    .0384125
           countywide |  -.7741204   .3739954    -2.07   0.038    -1.507138   -.0411029
                _cons |   3.417904   .4848509     7.05   0.000     2.467614    4.368194
----------------------+----------------------------------------------------------------
             /lnalpha |  -.7843017   .6747747    -1.16   0.245    -2.106836    .5382325
----------------------+----------------------------------------------------------------
                alpha |   .4564383    .307993                      .1216222    1.712976
---------------------------------------------------------------------------------------

. predict model1
variable model1 already defined
r(110);

. drop model1

. predict model1
(option n assumed; predicted number of events)
(129,453 missing values generated)

. zinb count log_lag_mil POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv county
> wide, inflate(log_lag_mil POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv cou
> ntywide) vce(cluster state_county_agency)

Fitting constant-only model:

Iteration 0:   log pseudolikelihood =   -3686.21  (not concave)
Iteration 1:   log pseudolikelihood = -3467.3404  (not concave)
Iteration 2:   log pseudolikelihood = -3271.4495  (not concave)
Iteration 3:   log pseudolikelihood = -3208.3958  (not concave)
Iteration 4:   log pseudolikelihood = -3090.9862  
Iteration 5:   log pseudolikelihood = -2913.8569  
Iteration 6:   log pseudolikelihood = -2849.1384  
Iteration 7:   log pseudolikelihood = -2833.5512  
Iteration 8:   log pseudolikelihood =   -2828.38  
Iteration 9:   log pseudolikelihood =  -2828.186  
Iteration 10:  log pseudolikelihood = -2828.1854  
Iteration 11:  log pseudolikelihood = -2828.1854  

Fitting full model:

Iteration 0:   log pseudolikelihood = -2828.1854  
Iteration 1:   log pseudolikelihood = -2585.9263  
Iteration 2:   log pseudolikelihood = -2564.3422  
Iteration 3:   log pseudolikelihood = -2563.5026  
Iteration 4:   log pseudolikelihood = -2563.4742  
Iteration 5:   log pseudolikelihood =  -2563.474  

Zero-inflated negative binomial regression      Number of obs     =     11,848
                                                Nonzero obs       =        716
                                                Zero obs          =     11,132

Inflation model      = logit                    Wald chi2(8)      =     213.85
Log pseudolikelihood = -2563.474                Prob > chi2       =     0.0000

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
count                 |
          log_lag_mil |   .0901626   .0402121     2.24   0.025     .0113483    .1689769
           POP2012div |   .0049704   .0022623     2.20   0.028     .0005363    .0094046
      percent_poverty |   .0039547    .017058     0.23   0.817    -.0294783    .0373878
         percent_hisp |   .0125511   .0087288     1.44   0.150    -.0045571    .0296593
percent_black_nothisp |   .0040942   .0053499     0.77   0.444    -.0063914    .0145797
           crime_rate |   .0198072   .0081261     2.44   0.015     .0038804     .035734
      budgetpercapdiv |  -.0012254    .021034    -0.06   0.954    -.0424513    .0400005
           countywide |  -1.531401    .215222    -7.12   0.000    -1.953229   -1.109574
                _cons |  -1.489189   .5312039    -2.80   0.005     -2.53033   -.4480484
----------------------+----------------------------------------------------------------
inflate               |
          log_lag_mil |  -.0108074   .0664471    -0.16   0.871    -.1410414    .1194265
           POP2012div |  -.2132065   .0629068    -3.39   0.001    -.3365016   -.0899114
      percent_poverty |  -.0057006   .0223611    -0.25   0.799    -.0495275    .0381264
         percent_hisp |   .0081196   .0129903     0.63   0.532    -.0173409    .0335801
percent_black_nothisp |  -.0024165   .0074325    -0.33   0.745     -.016984     .012151
           crime_rate |   .0065342   .0139566     0.47   0.640    -.0208201    .0338886
      budgetpercapdiv |   -.020751   .0529558    -0.39   0.695    -.1245423    .0830404
           countywide |  -.7553103   .4516928    -1.67   0.094    -1.640612    .1299912
                _cons |    3.33087   .7168662     4.65   0.000     1.925838    4.735902
----------------------+----------------------------------------------------------------
             /lnalpha |  -.8510938   .7255333    -1.17   0.241    -2.273113    .5709254
----------------------+----------------------------------------------------------------
                alpha |   .4269477   .3097648                      .1029911    1.769904
---------------------------------------------------------------------------------------

. zinb count lag_total_items POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv co
> untywide, inflate(lag_total_items POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetperca
> pdiv countywide) vce(cluster state_county_agency)

Fitting constant-only model:

Iteration 0:   log pseudolikelihood =   -3686.21  (not concave)
Iteration 1:   log pseudolikelihood = -3467.9384  
Iteration 2:   log pseudolikelihood = -3295.4606  
Iteration 3:   log pseudolikelihood =  -3145.714  
Iteration 4:   log pseudolikelihood = -2961.0121  
Iteration 5:   log pseudolikelihood = -2880.5708  
Iteration 6:   log pseudolikelihood = -2836.4801  
Iteration 7:   log pseudolikelihood = -2829.0293  
Iteration 8:   log pseudolikelihood = -2827.4629  
Iteration 9:   log pseudolikelihood = -2827.4569  
Iteration 10:  log pseudolikelihood = -2827.4569  

Fitting full model:

Iteration 0:   log pseudolikelihood = -2827.4569  
Iteration 1:   log pseudolikelihood = -2590.5141  
Iteration 2:   log pseudolikelihood = -2571.4295  
Iteration 3:   log pseudolikelihood = -2570.6879  
Iteration 4:   log pseudolikelihood = -2570.6559  
Iteration 5:   log pseudolikelihood = -2570.6559  

Zero-inflated negative binomial regression      Number of obs     =     11,848
                                                Nonzero obs       =        716
                                                Zero obs          =     11,132

Inflation model      = logit                    Wald chi2(8)      =     190.53
Log pseudolikelihood = -2570.656                Prob > chi2       =     0.0000

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
count                 |
      lag_total_items |   .0001823   .0003125     0.58   0.560    -.0004301    .0007948
           POP2012div |   .0051322   .0022626     2.27   0.023     .0006975    .0095669
      percent_poverty |   .0045449   .0160045     0.28   0.776    -.0268233    .0359131
         percent_hisp |   .0125115    .007701     1.62   0.104    -.0025821    .0276051
percent_black_nothisp |   .0040024   .0057129     0.70   0.484    -.0071946    .0151994
           crime_rate |   .0252349   .0086804     2.91   0.004     .0082216    .0422482
      budgetpercapdiv |  -.0004212   .0156807    -0.03   0.979    -.0311547    .0303123
           countywide |   -1.53731    .211657    -7.26   0.000     -1.95215    -1.12247
                _cons |  -1.361746   .4367942    -3.12   0.002    -2.217847   -.5056453
----------------------+----------------------------------------------------------------
inflate               |
      lag_total_items |   -.007477   .0032284    -2.32   0.021    -.0138045   -.0011494
           POP2012div |  -.2077385   .0463519    -4.48   0.000    -.2985866   -.1168904
      percent_poverty |  -.0035191   .0206344    -0.17   0.865    -.0439618    .0369236
         percent_hisp |   .0072404   .0103287     0.70   0.483    -.0130036    .0274843
percent_black_nothisp |  -.0025885   .0076392    -0.34   0.735    -.0175611    .0123842
           crime_rate |   .0141597   .0146346     0.97   0.333    -.0145236     .042843
      budgetpercapdiv |  -.0187548   .0328683    -0.57   0.568    -.0831755    .0456659
           countywide |  -.7554674   .4208744    -1.79   0.073    -1.580366    .0694312
                _cons |   3.367289   .5485707     6.14   0.000      2.29211    4.442468
----------------------+----------------------------------------------------------------
             /lnalpha |  -.8434605   .5950358    -1.42   0.156    -2.009709    .3227883
----------------------+----------------------------------------------------------------
                alpha |   .4302192   .2559958                      .1340276    1.380973
---------------------------------------------------------------------------------------

. zinb count lag_hv50 POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv countywid
> e, inflate(lag_hv50 POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv countywid
> e) vce(cluster state_county_agency)

Fitting constant-only model:

Iteration 0:   log pseudolikelihood =   -3686.21  (not concave)
Iteration 1:   log pseudolikelihood = -3467.4485  
Iteration 2:   log pseudolikelihood = -3318.4534  
Iteration 3:   log pseudolikelihood = -3074.1117  
Iteration 4:   log pseudolikelihood = -2944.2024  
Iteration 5:   log pseudolikelihood = -2871.8936  
Iteration 6:   log pseudolikelihood = -2838.1453  
Iteration 7:   log pseudolikelihood = -2831.7292  
Iteration 8:   log pseudolikelihood = -2830.9406  
Iteration 9:   log pseudolikelihood = -2830.9391  
Iteration 10:  log pseudolikelihood = -2830.9391  

Fitting full model:

Iteration 0:   log pseudolikelihood = -2830.9391  
Iteration 1:   log pseudolikelihood = -2588.5246  
Iteration 2:   log pseudolikelihood = -2565.8463  
Iteration 3:   log pseudolikelihood =  -2564.921  
Iteration 4:   log pseudolikelihood = -2564.8959  
Iteration 5:   log pseudolikelihood = -2564.8959  

Zero-inflated negative binomial regression      Number of obs     =     11,848
                                                Nonzero obs       =        716
                                                Zero obs          =     11,132

Inflation model      = logit                    Wald chi2(8)      =     183.63
Log pseudolikelihood = -2564.896                Prob > chi2       =     0.0000

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
count                 |
             lag_hv50 |   .4038422   .1580276     2.56   0.011     .0941139    .7135706
           POP2012div |   .0052315   .0021788     2.40   0.016     .0009612    .0095019
      percent_poverty |   .0038775   .0164747     0.24   0.814    -.0284122    .0361673
         percent_hisp |   .0112747   .0079039     1.43   0.154    -.0042167    .0267661
percent_black_nothisp |   .0040897   .0054569     0.75   0.454    -.0066056     .014785
           crime_rate |   .0197246   .0085535     2.31   0.021     .0029601    .0364892
      budgetpercapdiv |   .0004083    .014429     0.03   0.977     -.027872    .0286886
           countywide |  -1.567604   .1937095    -8.09   0.000    -1.947268   -1.187941
                _cons |  -1.470984   .4471102    -3.29   0.001    -2.347304   -.5946637
----------------------+----------------------------------------------------------------
inflate               |
             lag_hv50 |   .2113971   .2654332     0.80   0.426    -.3088423    .7316366
           POP2012div |  -.2198945   .0553464    -3.97   0.000    -.3283715   -.1114175
      percent_poverty |  -.0056272   .0210365    -0.27   0.789     -.046858    .0356035
         percent_hisp |   .0062326   .0112403     0.55   0.579    -.0157979    .0282631
percent_black_nothisp |  -.0023531   .0075662    -0.31   0.756    -.0171826    .0124764
           crime_rate |   .0051043   .0141528     0.36   0.718    -.0226346    .0328432
      budgetpercapdiv |  -.0165668   .0304875    -0.54   0.587    -.0763211    .0431876
           countywide |   -.819069   .3893589    -2.10   0.035    -1.582198   -.0559395
                _cons |   3.290457   .5195027     6.33   0.000      2.27225    4.308663
----------------------+----------------------------------------------------------------
             /lnalpha |  -.8139866    .614294    -1.33   0.185    -2.017981    .3900075
----------------------+----------------------------------------------------------------
                alpha |   .4430881   .2721864                      .1329236    1.476992
---------------------------------------------------------------------------------------

. zinb count lag_hv100 POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv countywi
> de, inflate(lag_hv100 POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv countyw
> ide) vce(cluster state_county_agency)

Fitting constant-only model:

Iteration 0:   log pseudolikelihood =   -3686.21  (not concave)
Iteration 1:   log pseudolikelihood = -3467.6538  
Iteration 2:   log pseudolikelihood = -3324.6736  
Iteration 3:   log pseudolikelihood = -3074.4858  
Iteration 4:   log pseudolikelihood = -2940.3173  
Iteration 5:   log pseudolikelihood = -2867.7931  
Iteration 6:   log pseudolikelihood = -2836.6543  
Iteration 7:   log pseudolikelihood = -2830.4816  
Iteration 8:   log pseudolikelihood = -2829.2577  
Iteration 9:   log pseudolikelihood =  -2829.253  
Iteration 10:  log pseudolikelihood =  -2829.253  

Fitting full model:

Iteration 0:   log pseudolikelihood =  -2829.253  
Iteration 1:   log pseudolikelihood = -2582.3638  
Iteration 2:   log pseudolikelihood = -2555.6924  
Iteration 3:   log pseudolikelihood = -2554.5503  
Iteration 4:   log pseudolikelihood = -2554.5218  
Iteration 5:   log pseudolikelihood = -2554.5218  

Zero-inflated negative binomial regression      Number of obs     =     11,848
                                                Nonzero obs       =        716
                                                Zero obs          =     11,132

Inflation model      = logit                    Wald chi2(8)      =     236.64
Log pseudolikelihood = -2554.522                Prob > chi2       =     0.0000

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
count                 |
            lag_hv100 |   .6005014   .1584319     3.79   0.000     .2899805    .9110223
           POP2012div |   .0048179    .001501     3.21   0.001      .001876    .0077597
      percent_poverty |   .0099147   .0149213     0.66   0.506    -.0193306      .03916
         percent_hisp |   .0119705   .0059186     2.02   0.043     .0003702    .0235707
percent_black_nothisp |   .0023526   .0047654     0.49   0.622    -.0069873    .0116926
           crime_rate |   .0205996   .0073774     2.79   0.005     .0061403     .035059
      budgetpercapdiv |   .0030709   .0121022     0.25   0.800    -.0206489    .0267907
           countywide |  -1.578502   .1855367    -8.51   0.000    -1.942147   -1.214856
                _cons |  -1.577006    .374131    -4.22   0.000    -2.310289   -.8437223
----------------------+----------------------------------------------------------------
inflate               |
            lag_hv100 |   .2912494   .3052218     0.95   0.340    -.3069744    .8894732
           POP2012div |   -.216176   .0354362    -6.10   0.000    -.2856298   -.1467222
      percent_poverty |   .0013772   .0190883     0.07   0.942    -.0360353    .0387897
         percent_hisp |    .008126   .0086165     0.94   0.346     -.008762    .0250139
percent_black_nothisp |  -.0049981   .0072386    -0.69   0.490    -.0191856    .0091894
           crime_rate |   .0073559   .0132192     0.56   0.578    -.0185533    .0332652
      budgetpercapdiv |  -.0139888   .0218112    -0.64   0.521     -.056738    .0287603
           countywide |  -.8291067   .3710801    -2.23   0.025     -1.55641    -.101803
                _cons |   3.131342   .4690064     6.68   0.000     2.212106    4.050577
----------------------+----------------------------------------------------------------
             /lnalpha |  -1.005297   .4985052    -2.02   0.044    -1.982349   -.0282451
----------------------+----------------------------------------------------------------
                alpha |   .3659358   .1824209                      .1377452    .9721501
---------------------------------------------------------------------------------------

. zinb count lag_total_items POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv co
> untywide lag_hv50, inflate(lag_total_items POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate bu
> dgetpercapdiv countywide lag_hv50) vce(cluster state_county_agency)

Fitting constant-only model:

Iteration 0:   log pseudolikelihood =   -3686.21  (not concave)
Iteration 1:   log pseudolikelihood = -3467.3666  
Iteration 2:   log pseudolikelihood = -3320.1637  
Iteration 3:   log pseudolikelihood = -3075.0832  
Iteration 4:   log pseudolikelihood = -2941.6675  
Iteration 5:   log pseudolikelihood = -2866.1461  
Iteration 6:   log pseudolikelihood = -2831.8297  
Iteration 7:   log pseudolikelihood = -2826.7267  
Iteration 8:   log pseudolikelihood = -2826.4316  
Iteration 9:   log pseudolikelihood = -2826.4311  
Iteration 10:  log pseudolikelihood = -2826.4311  

Fitting full model:

Iteration 0:   log pseudolikelihood = -2826.4311  
Iteration 1:   log pseudolikelihood =  -2587.565  
Iteration 2:   log pseudolikelihood = -2563.7191  
Iteration 3:   log pseudolikelihood = -2562.6723  
Iteration 4:   log pseudolikelihood = -2562.4591  
Iteration 5:   log pseudolikelihood = -2562.4589  
Iteration 6:   log pseudolikelihood = -2562.4589  

Zero-inflated negative binomial regression      Number of obs     =     11,848
                                                Nonzero obs       =        716
                                                Zero obs          =     11,132

Inflation model      = logit                    Wald chi2(9)      =     201.36
Log pseudolikelihood = -2562.459                Prob > chi2       =     0.0000

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
count                 |
      lag_total_items |  -.0000462   .0003573    -0.13   0.897    -.0007466    .0006541
           POP2012div |   .0053084    .002406     2.21   0.027     .0005926    .0100241
      percent_poverty |   .0069045   .0168232     0.41   0.682    -.0260684    .0398773
         percent_hisp |   .0118533   .0075245     1.58   0.115    -.0028944     .026601
percent_black_nothisp |   .0037498   .0057735     0.65   0.516    -.0075661    .0150656
           crime_rate |   .0211396   .0083522     2.53   0.011     .0047696    .0375097
      budgetpercapdiv |  -.0001671   .0159562    -0.01   0.992    -.0314407    .0311066
           countywide |  -1.563119   .2171368    -7.20   0.000    -1.988699   -1.137538
             lag_hv50 |   .3978148   .1514115     2.63   0.009     .1010536    .6945759
                _cons |  -1.539847    .451144    -3.41   0.001    -2.424073   -.6556208
----------------------+----------------------------------------------------------------
inflate               |
      lag_total_items |  -.0072514   .0037297    -1.94   0.052    -.0145615    .0000587
           POP2012div |  -.2134537   .0458393    -4.66   0.000    -.3032971   -.1236102
      percent_poverty |  -.0006728    .022034    -0.03   0.976    -.0438587    .0425131
         percent_hisp |   .0069709   .0102862     0.68   0.498    -.0131898    .0271316
percent_black_nothisp |  -.0030838   .0078045    -0.40   0.693    -.0183803    .0122126
           crime_rate |   .0117306   .0145096     0.81   0.419    -.0167076    .0401688
      budgetpercapdiv |  -.0187575   .0358226    -0.52   0.601    -.0889685    .0514535
           countywide |  -.8244355   .4484058    -1.84   0.066    -1.703295    .0544237
             lag_hv50 |   .3835621    .278568     1.38   0.169    -.1624211    .9295453
                _cons |    3.20422   .6048362     5.30   0.000     2.018762    4.389677
----------------------+----------------------------------------------------------------
             /lnalpha |  -.8069977   .5429875    -1.49   0.137    -1.871234    .2572381
----------------------+----------------------------------------------------------------
                alpha |   .4461957   .2422786                      .1539337    1.293353
---------------------------------------------------------------------------------------

. zinb count lag_total_items POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv co
> untywide lag_hv100, inflate(lag_total_items POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate b
> udgetpercapdiv countywide lag_hv100) vce(cluster state_county_agency)

Fitting constant-only model:

Iteration 0:   log pseudolikelihood =   -3686.21  (not concave)
Iteration 1:   log pseudolikelihood = -3467.5667  
Iteration 2:   log pseudolikelihood = -3326.6096  
Iteration 3:   log pseudolikelihood =  -3076.267  
Iteration 4:   log pseudolikelihood = -2937.3017  
Iteration 5:   log pseudolikelihood = -2860.7049  
Iteration 6:   log pseudolikelihood = -2832.9834  
Iteration 7:   log pseudolikelihood = -2826.1529  
Iteration 8:   log pseudolikelihood = -2824.8075  
Iteration 9:   log pseudolikelihood =  -2824.804  
Iteration 10:  log pseudolikelihood =  -2824.804  

Fitting full model:

Iteration 0:   log pseudolikelihood =  -2824.804  
Iteration 1:   log pseudolikelihood =  -2581.334  
Iteration 2:   log pseudolikelihood = -2552.8716  
Iteration 3:   log pseudolikelihood = -2551.4396  
Iteration 4:   log pseudolikelihood = -2551.3845  
Iteration 5:   log pseudolikelihood = -2551.3844  

Zero-inflated negative binomial regression      Number of obs     =     11,848
                                                Nonzero obs       =        716
                                                Zero obs          =     11,132

Inflation model      = logit                    Wald chi2(9)      =     249.29
Log pseudolikelihood = -2551.384                Prob > chi2       =     0.0000

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
count                 |
      lag_total_items |  -.0001142   .0003393    -0.34   0.736    -.0007791    .0005507
           POP2012div |   .0050127   .0018273     2.74   0.006     .0014312    .0085941
      percent_poverty |      .0129   .0153743     0.84   0.401     -.017233     .043033
         percent_hisp |   .0121847   .0056009     2.18   0.030     .0012072    .0231623
percent_black_nothisp |   .0018085   .0052092     0.35   0.728    -.0084014    .0120183
           crime_rate |   .0212923   .0071965     2.96   0.003     .0071874    .0353973
      budgetpercapdiv |   .0029358   .0122535     0.24   0.811    -.0210806    .0269521
           countywide |  -1.586263    .194982    -8.14   0.000    -1.968421   -1.204106
            lag_hv100 |   .6193365   .1483996     4.17   0.000     .3284787    .9101943
                _cons |  -1.644301   .3773198    -4.36   0.000    -2.383834   -.9047678
----------------------+----------------------------------------------------------------
inflate               |
      lag_total_items |  -.0068921     .00324    -2.13   0.033    -.0132425   -.0005418
           POP2012div |  -.2114887   .0304169    -6.95   0.000    -.2711047   -.1518726
      percent_poverty |   .0061237   .0197412     0.31   0.756    -.0325684    .0448158
         percent_hisp |    .008173    .007725     1.06   0.290    -.0069678    .0233137
percent_black_nothisp |  -.0057843   .0075164    -0.77   0.442    -.0205162    .0089476
           crime_rate |   .0129486   .0137729     0.94   0.347    -.0140457     .039943
      budgetpercapdiv |  -.0146779   .0217377    -0.68   0.500     -.057283    .0279273
           countywide |  -.8449695     .39689    -2.13   0.033     -1.62286   -.0670794
            lag_hv100 |    .472651   .3105263     1.52   0.128    -.1359693    1.081271
                _cons |   3.065575   .4875054     6.29   0.000     2.110082    4.021068
----------------------+----------------------------------------------------------------
             /lnalpha |  -.9771809    .446035    -2.19   0.028    -1.851393   -.1029684
----------------------+----------------------------------------------------------------
                alpha |   .3763706   .1678745                      .1570182    .9021555
---------------------------------------------------------------------------------------

. zinb count tactical_operations POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdi
> v countywide, inflate(tactical_operations POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate bud
> getpercapdiv countywide) vce(cluster state_county_agency)

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -4212.8607  (not concave)
Iteration 1:   log pseudolikelihood = -3962.0209  (not concave)
Iteration 2:   log pseudolikelihood = -3724.1898  (not concave)
Iteration 3:   log pseudolikelihood = -3648.9125  (not concave)
Iteration 4:   log pseudolikelihood = -3489.5097  
Iteration 5:   log pseudolikelihood = -3469.4333  
Iteration 6:   log pseudolikelihood = -3284.2031  
Iteration 7:   log pseudolikelihood = -3264.7632  
Iteration 8:   log pseudolikelihood =  -3215.482  
Iteration 9:   log pseudolikelihood = -3214.1924  
Iteration 10:  log pseudolikelihood = -3214.1855  
Iteration 11:  log pseudolikelihood = -3214.1855  

Fitting full model:

Iteration 0:   log pseudolikelihood = -3214.1855  
Iteration 1:   log pseudolikelihood =  -2964.051  
Iteration 2:   log pseudolikelihood = -2936.5202  
Iteration 3:   log pseudolikelihood =  -2934.729  
Iteration 4:   log pseudolikelihood = -2934.6311  
Iteration 5:   log pseudolikelihood = -2934.6308  
Iteration 6:   log pseudolikelihood = -2934.6308  

Zero-inflated negative binomial regression      Number of obs     =     13,329
                                                Nonzero obs       =        821
                                                Zero obs          =     12,508

Inflation model      = logit                    Wald chi2(8)      =     146.78
Log pseudolikelihood = -2934.631                Prob > chi2       =     0.0000

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
count                 |
  tactical_operations |   .7257063   .4619805     1.57   0.116    -.1797589    1.631172
           POP2012div |   .0053928   .0019678     2.74   0.006      .001536    .0092496
      percent_poverty |   .0041219   .0160564     0.26   0.797    -.0273481    .0355918
         percent_hisp |   .0116227   .0070422     1.65   0.099    -.0021798    .0254252
percent_black_nothisp |   .0043982   .0055098     0.80   0.425    -.0064008    .0151972
           crime_rate |   .0212154   .0080205     2.65   0.008     .0054955    .0369354
      budgetpercapdiv |  -.0013908   .0122814    -0.11   0.910    -.0254618    .0226802
           countywide |  -1.470823   .1991495    -7.39   0.000    -1.861149   -1.080497
                _cons |  -1.994473   .5577504    -3.58   0.000    -3.087644   -.9013023
----------------------+----------------------------------------------------------------
inflate               |
  tactical_operations |  -.3172194   .4430861    -0.72   0.474    -1.185652    .5512134
           POP2012div |  -.1860463   .0447201    -4.16   0.000    -.2736961   -.0983964
      percent_poverty |   -.007654   .0211733    -0.36   0.718    -.0491529    .0338448
         percent_hisp |   .0045326   .0096388     0.47   0.638    -.0143591    .0234243
percent_black_nothisp |  -.0013031   .0074041    -0.18   0.860    -.0158149    .0132087
           crime_rate |   .0025938    .012119     0.21   0.831    -.0211589    .0263466
      budgetpercapdiv |  -.0169004   .0262744    -0.64   0.520    -.0683974    .0345965
           countywide |  -.7929447   .3907394    -2.03   0.042     -1.55878   -.0271095
                _cons |    3.39328   .6268655     5.41   0.000     2.164646    4.621914
----------------------+----------------------------------------------------------------
             /lnalpha |  -.7797211   .5125719    -1.52   0.128    -1.784344    .2249014
----------------------+----------------------------------------------------------------
                alpha |   .4585339   .2350316                      .1679072    1.252199
---------------------------------------------------------------------------------------

. nbreg count lag_mil_div POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv count
> ywide, vce(cluster state_county_agency)

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -60237.194  (not concave)
Iteration 1:   log pseudolikelihood = -50599.455  (not concave)
Iteration 2:   log pseudolikelihood =  -48576.74  
Iteration 3:   log pseudolikelihood = -26535.041  (backed up)
Iteration 4:   log pseudolikelihood = -12925.714  (backed up)
Iteration 5:   log pseudolikelihood = -8420.6775  (backed up)
Iteration 6:   log pseudolikelihood = -5382.3869  
Iteration 7:   log pseudolikelihood = -5040.4586  
Iteration 8:   log pseudolikelihood = -3320.3545  
Iteration 9:   log pseudolikelihood = -3242.1413  
Iteration 10:  log pseudolikelihood = -3241.7758  
Iteration 11:  log pseudolikelihood = -3241.7757  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -3679.7687  (not concave)
Iteration 1:   log pseudolikelihood = -3398.5921  
Iteration 2:   log pseudolikelihood = -3357.2621  
Iteration 3:   log pseudolikelihood = -3351.8409  
Iteration 4:   log pseudolikelihood = -3351.8341  
Iteration 5:   log pseudolikelihood = -3351.8341  

Fitting full model:

Iteration 0:   log pseudolikelihood = -3123.1415  
Iteration 1:   log pseudolikelihood = -2985.7375  
Iteration 2:   log pseudolikelihood =  -2903.146  
Iteration 3:   log pseudolikelihood =  -2887.697  
Iteration 4:   log pseudolikelihood = -2887.6208  
Iteration 5:   log pseudolikelihood = -2887.6208  

Negative binomial regression                    Number of obs     =     11,848
                                                Wald chi2(8)      =     187.66
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -2887.6208               Pseudo R2         =     0.1385

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
          lag_mil_div |   .0032939   .0011592     2.84   0.004     .0010219    .0055658
           POP2012div |   .0159669   .0040019     3.99   0.000     .0081233    .0238105
      percent_poverty |   .0012352    .006745     0.18   0.855    -.0119848    .0144553
         percent_hisp |   .0139149   .0028891     4.82   0.000     .0082524    .0195774
percent_black_nothisp |   .0155867   .0032069     4.86   0.000     .0093014    .0218721
           crime_rate |   .0031374   .0070866     0.44   0.658    -.0107521     .017027
      budgetpercapdiv |   .0276753   .0102022     2.71   0.007     .0076794    .0476712
           countywide |  -.8344617   .1617666    -5.16   0.000    -1.151518   -.5174051
                _cons |  -3.756468   .1821866   -20.62   0.000    -4.113547   -3.399389
----------------------+----------------------------------------------------------------
             /lnalpha |   1.212703   .1663092                      .8867428    1.538663
----------------------+----------------------------------------------------------------
                alpha |   3.362561   .5592249                      2.427211    4.658358
---------------------------------------------------------------------------------------

. zip count tactical_operations POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv
>  countywide, inflate(tactical_operations POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budg
> etpercapdiv countywide) vce(cluster state_county_agency)

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -4574.4239  (not concave)
Iteration 1:   log pseudolikelihood = -4113.5996  (not concave)
Iteration 2:   log pseudolikelihood = -3867.8238  
Iteration 3:   log pseudolikelihood = -3550.2487  
Iteration 4:   log pseudolikelihood = -3354.6421  
Iteration 5:   log pseudolikelihood = -3330.8838  
Iteration 6:   log pseudolikelihood = -3329.5444  
Iteration 7:   log pseudolikelihood =  -3329.534  
Iteration 8:   log pseudolikelihood =  -3329.534  

Fitting full model:

Iteration 0:   log pseudolikelihood =  -3329.534  (not concave)
Iteration 1:   log pseudolikelihood = -3144.4806  (not concave)
Iteration 2:   log pseudolikelihood = -3049.9285  
Iteration 3:   log pseudolikelihood = -2980.9589  
Iteration 4:   log pseudolikelihood = -2958.6192  
Iteration 5:   log pseudolikelihood = -2958.2973  
Iteration 6:   log pseudolikelihood = -2958.2971  

Zero-inflated Poisson regression                Number of obs     =     13,329
                                                Nonzero obs       =        821
                                                Zero obs          =     12,508

Inflation model      = logit                    Wald chi2(8)      =     180.27
Log pseudolikelihood = -2958.297                Prob > chi2       =     0.0000

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
count                 |
  tactical_operations |   .7568996   .4642166     1.63   0.103    -.1529483    1.666748
           POP2012div |   .0043288   .0004043    10.71   0.000     .0035364    .0051213
      percent_poverty |  -.0029454   .0149479    -0.20   0.844    -.0322427    .0263518
         percent_hisp |   .0151157   .0041602     3.63   0.000     .0069619    .0232695
percent_black_nothisp |   .0067391   .0049816     1.35   0.176    -.0030248    .0165029
           crime_rate |   .0202024   .0083213     2.43   0.015     .0038931    .0365118
      budgetpercapdiv |  -.0101203   .0102574    -0.99   0.324    -.0302244    .0099837
           countywide |  -1.575984   .2218873    -7.10   0.000    -2.010875   -1.141093
                _cons |   -1.60144   .5900629    -2.71   0.007    -2.757942   -.4449383
----------------------+----------------------------------------------------------------
inflate               |
  tactical_operations |     -.4023   .4679474    -0.86   0.390     -1.31946    .5148601
           POP2012div |  -.1420862    .018199    -7.81   0.000    -.1777555   -.1064169
      percent_poverty |  -.0158919   .0190051    -0.84   0.403    -.0531412    .0213573
         percent_hisp |    .008785   .0054919     1.60   0.110     -.001979    .0195489
percent_black_nothisp |   .0005905   .0066745     0.09   0.929    -.0124913    .0136724
           crime_rate |   .0018913   .0106772     0.18   0.859    -.0190357    .0228183
      budgetpercapdiv |  -.0380924    .023484    -1.62   0.105    -.0841201    .0079353
           countywide |     -.8196   .4098845    -2.00   0.046    -1.622959   -.0162412
                _cons |   3.859666   .6513388     5.93   0.000     2.583065    5.136267
---------------------------------------------------------------------------------------

. poisson count lag_mil_div POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv cou
> ntywide, vce(cluster state_county_agency)

Iteration 0:   log pseudolikelihood = -60237.194  (not concave)
Iteration 1:   log pseudolikelihood = -50599.455  (not concave)
Iteration 2:   log pseudolikelihood =  -48576.74  
Iteration 3:   log pseudolikelihood = -26535.041  (backed up)
Iteration 4:   log pseudolikelihood = -12925.714  (backed up)
Iteration 5:   log pseudolikelihood = -8420.6775  (backed up)
Iteration 6:   log pseudolikelihood = -5382.3869  
Iteration 7:   log pseudolikelihood = -5040.4586  
Iteration 8:   log pseudolikelihood = -3320.3545  
Iteration 9:   log pseudolikelihood = -3242.1413  
Iteration 10:  log pseudolikelihood = -3241.7758  
Iteration 11:  log pseudolikelihood = -3241.7757  

Poisson regression                              Number of obs     =     11,848
                                                Wald chi2(8)      =     344.86
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -3241.7757               Pseudo R2         =     0.1919

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
          lag_mil_div |   .0022022   .0005102     4.32   0.000     .0012022    .0032022
           POP2012div |    .004172   .0008227     5.07   0.000     .0025596    .0057844
      percent_poverty |  -.0029337   .0075582    -0.39   0.698    -.0177475    .0118801
         percent_hisp |   .0242984   .0029914     8.12   0.000     .0184353    .0301615
percent_black_nothisp |   .0239749   .0035171     6.82   0.000     .0170815    .0308683
           crime_rate |  -.0072011   .0095077    -0.76   0.449    -.0258359    .0114336
      budgetpercapdiv |   .0189317   .0059032     3.21   0.001     .0073617    .0305018
           countywide |  -.7818593   .2309871    -3.38   0.001    -1.234586   -.3291329
                _cons |  -3.462364   .1598467   -21.66   0.000    -3.775658    -3.14907
---------------------------------------------------------------------------------------

. xtset state_county_agency syearquarter
       panel variable:  state_county_agency (unbalanced)
        time variable:  syearquarter, 1960q2 to 1962q2
                delta:  1 quarter

. xtreg count lag_mil_div POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv count
> ywide, re

Random-effects GLS regression                   Number of obs     =     11,848
Group variable: state_coun~y                    Number of groups  =      1,481

R-sq:                                           Obs per group:
     within  = 0.0000                                         min =          8
     between = 0.5003                                         avg =        8.0
     overall = 0.2948                                         max =          8

                                                Wald chi2(8)      =    1466.52
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

---------------------------------------------------------------------------------------
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
          lag_mil_div |   .0010056   .0001099     9.15   0.000     .0007901     .001221
           POP2012div |   .0056484   .0001825    30.94   0.000     .0052906    .0060061
      percent_poverty |   .0011724   .0009026     1.30   0.194    -.0005967    .0029414
         percent_hisp |   .0012075   .0003762     3.21   0.001     .0004702    .0019448
percent_black_nothisp |  -.0000224   .0004463    -0.05   0.960    -.0008972    .0008523
           crime_rate |   .0005928   .0008109     0.73   0.465    -.0009965    .0021821
      budgetpercapdiv |  -.0013902   .0009035    -1.54   0.124     -.003161    .0003807
           countywide |  -.1230057   .0150418    -8.18   0.000    -.1524871   -.0935243
                _cons |   .0208015   .0205928     1.01   0.312    -.0195597    .0611627
----------------------+----------------------------------------------------------------
              sigma_u |  .22710069
              sigma_e |  .31888129
                  rho |  .33651784   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------

. regress count lag_mil_div POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv cou
> ntywide, vce(cluster state_county_agency)

Linear regression                               Number of obs     =     11,848
                                                F(8, 1480)        =      11.18
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2950
                                                Root MSE          =     .39122

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
          lag_mil_div |   .0011053   .0005702     1.94   0.053    -.0000131    .0022238
           POP2012div |   .0055977   .0013114     4.27   0.000     .0030253    .0081701
      percent_poverty |   .0011979   .0005193     2.31   0.021     .0001792    .0022166
         percent_hisp |   .0011941   .0004066     2.94   0.003     .0003965    .0019917
percent_black_nothisp |  -.0000299   .0004362    -0.07   0.945    -.0008855    .0008257
           crime_rate |   .0005287   .0005651     0.94   0.350    -.0005798    .0016372
      budgetpercapdiv |  -.0014149   .0014122    -1.00   0.317     -.004185    .0013552
           countywide |  -.1234862   .0220094    -5.61   0.000    -.1666592   -.0803133
                _cons |   .0203303   .0165817     1.23   0.220    -.0121959    .0528565
---------------------------------------------------------------------------------------

. regress total_div lag_count POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv c
> ountywide, vce(cluster state_county_agency)

Linear regression                               Number of obs     =     11,848
                                                F(8, 1480)        =       6.36
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1575
                                                Root MSE          =     55.798

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
            total_div |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
            lag_count |   21.59868   13.09521     1.65   0.099    -4.088465    47.28583
           POP2012div |   .3658797   .1114674     3.28   0.001     .1472288    .5845306
      percent_poverty |  -.2540669   .1396125    -1.82   0.069    -.5279262    .0197925
         percent_hisp |   .1002338   .0625608     1.60   0.109    -.0224835     .222951
percent_black_nothisp |   .0456481   .0769025     0.59   0.553    -.1052014    .1964977
           crime_rate |   .7389964   .1801723     4.10   0.000     .3855761    1.092417
      budgetpercapdiv |   .2802111   .2446652     1.15   0.252    -.1997164    .7601386
           countywide |   7.567977   3.003009     2.52   0.012     1.677371    13.45858
                _cons |   3.395851   3.737052     0.91   0.364    -3.934631    10.72633
---------------------------------------------------------------------------------------

. xtreg total_div lag_count POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv cou
> ntywide, re

Random-effects GLS regression                   Number of obs     =     11,848
Group variable: state_coun~y                    Number of groups  =      1,481

R-sq:                                           Obs per group:
     within  = 0.0002                                         min =          8
     between = 0.1412                                         avg =        8.0
     overall = 0.1366                                         max =          8

                                                Wald chi2(8)      =     251.68
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

---------------------------------------------------------------------------------------
            total_div |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
            lag_count |   .0929607   .3546133     0.26   0.793    -.6020686    .7879899
           POP2012div |   .5062596    .037176    13.62   0.000     .4333961    .5791232
      percent_poverty |  -.1917243   .1787673    -1.07   0.284    -.5421019    .1586532
         percent_hisp |    .129821   .0801581     1.62   0.105    -.0272861     .286928
percent_black_nothisp |   .0469149    .094392     0.50   0.619    -.1380901    .2319198
           crime_rate |   .7138841   .1707782     4.18   0.000     .3791649    1.048603
      budgetpercapdiv |   .2489647   .1926571     1.29   0.196    -.1286364    .6265657
           countywide |    4.92305   3.204991     1.54   0.125    -1.358618    11.20472
                _cons |   3.704048   4.283797     0.86   0.387    -4.692039    12.10014
----------------------+----------------------------------------------------------------
              sigma_u |  53.814758
              sigma_e |  11.665134
                  rho |   .9551218   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------

. clear

. use "C:\Users\eel19\Dropbox\Classes\Paper Projects\Police Militarization and Shootings\Data\finalized\Re-do\final-annu
> al.dta"

. gen budgetpercapdiv = budgetpercap/10000
(40,777 missing values generated)

. zinb count lag_mil_div POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv county
> wide, inflate(lag_mil_div POP2012div percent_poverty percent_hisp percent_black_nothisp crime_rate budgetpercapdiv cou
> ntywide) vce(cluster state_county_agency)

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -3243.5309  (not concave)
Iteration 1:   log pseudolikelihood = -2379.9209  
Iteration 2:   log pseudolikelihood = -2244.5852  
Iteration 3:   log pseudolikelihood = -2175.3456  
Iteration 4:   log pseudolikelihood = -2136.6232  
Iteration 5:   log pseudolikelihood = -2120.9161  
Iteration 6:   log pseudolikelihood =   -2117.32  
Iteration 7:   log pseudolikelihood = -2115.5441  
Iteration 8:   log pseudolikelihood = -2115.4154  
Iteration 9:   log pseudolikelihood = -2115.4149  
Iteration 10:  log pseudolikelihood = -2115.4149  

Fitting full model:

Iteration 0:   log pseudolikelihood = -2115.4149  
Iteration 1:   log pseudolikelihood = -2060.5097  
Iteration 2:   log pseudolikelihood = -1920.1718  (not concave)
Iteration 3:   log pseudolikelihood = -1874.8787  
Iteration 4:   log pseudolikelihood = -1836.3252  
Iteration 5:   log pseudolikelihood = -1828.5737  
Iteration 6:   log pseudolikelihood = -1827.6107  
Iteration 7:   log pseudolikelihood =  -1827.589  
Iteration 8:   log pseudolikelihood = -1827.5889  

Zero-inflated negative binomial regression      Number of obs     =      2,962
                                                Nonzero obs       =        497
                                                Zero obs          =      2,465

Inflation model      = logit                    Wald chi2(8)      =      88.35
Log pseudolikelihood = -1827.589                Prob > chi2       =     0.0000

                         (Std. Err. adjusted for 1,481 clusters in state_county_agency)
---------------------------------------------------------------------------------------
                      |               Robust
                count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
count                 |
          lag_mil_div |   .0105989     .00177     5.99   0.000     .0071296    .0140681
           POP2012div |   .0131841   .0036925     3.57   0.000     .0059468    .0204214
      percent_poverty |   .0152383   .0147875     1.03   0.303    -.0137446    .0442212
         percent_hisp |   .0028537   .0043299     0.66   0.510    -.0056326    .0113401
percent_black_nothisp |   .0018157   .0045962     0.40   0.693    -.0071926    .0108241
           crime_rate |   .0271605   .0086691     3.13   0.002     .0101695    .0441516
      budgetpercapdiv |   .0171374   .0087666     1.95   0.051    -.0000447    .0343196
           countywide |  -1.176053   .1952761    -6.02   0.000    -1.558787   -.7933185
                _cons |  -1.319312   .3076041    -4.29   0.000    -1.922205   -.7164191
----------------------+----------------------------------------------------------------
inflate               |
          lag_mil_div |    .006675   .0027306     2.44   0.015     .0013231     .012027
           POP2012div |  -.5762331   .1136299    -5.07   0.000    -.7989436   -.3535225
      percent_poverty |   .0020768   .0265361     0.08   0.938     -.049933    .0540866
         percent_hisp |   -.006915   .0081614    -0.85   0.397     -.022911     .009081
percent_black_nothisp |  -.0024653    .010048    -0.25   0.806    -.0221592    .0172285
           crime_rate |   .0079032    .015669     0.50   0.614    -.0228075     .038614
      budgetpercapdiv |   .0122771   .0207712     0.59   0.554    -.0284337    .0529879
           countywide |  -.1578388   .4660423    -0.34   0.735    -1.071265    .7555873
                _cons |   2.394882   .5295461     4.52   0.000     1.356991    3.432774
----------------------+----------------------------------------------------------------
             /lnalpha |    .689671   .1369183     5.04   0.000      .421316     .958026
----------------------+----------------------------------------------------------------
                alpha |    1.99306   .2728864                      1.523966    2.606546
---------------------------------------------------------------------------------------

. log close
      name:  <unnamed>
       log:  C:\Users\eel19\Dropbox\Classes\Paper Projects\Police Militarization and Shootings\Paper\PRQ Final Log.log
  log type:  text
 closed on:  23 May 2018, 15:58:03
------------------------------------------------------------------------------------------------------------------------
